WARNING : This video is not up to date!
If you want to see the last version of DeepAlgo live : ask a free demo on DeepAlgo.com
Deep Algo is a SaaS platform based on 100% automatic algorithm extraction technology.
This Tutorial explains how you can Understand the source code of a poker game.

published:22 Jul 2016

views:3561

From the statisticians forecasting sports scores to the intelligent bots beating human poker players, Adam Kucharski traces the scientific origins of the world's best gambling strategies.
Watch the Q&A here: https://www.youtube.com/watch?v=o0XxbHnf5ro
Subscribe for regular science videos: http://bit.ly/RiSubscRibe
Spanning mathematics, psychology, economics and physics, Adam Kucharski reveals the long and tangled history between betting and science, and explains how gambling shaped everything from probability to game theory, and chaos theory to artificial intelligence.
Adam Kucharski is a Lecturer at LondonSchool of Hygiene and Tropical Medicine where his research focusses on the dynamics of infectious diseases, particularly emerging infections. Prior to this, he got a degree in mathematics from the University of Warwick, received a PhD in applied mathematics from the University of Cambridge and had a post-doc position at Imperial College London.
The Ri is on Twitter: http://twitter.com/ri_science
and Facebook: http://www.facebook.com/royalinstitution
and Tumblr: http://ri-science.tumblr.com/
Our editorial policy: http://www.rigb.org/home/editorial-po...
Subscribe for the latest science videos: http://bit.ly/RiNewsletter

EP 086: What traders can learn about mental game, from world champion poker coach—Jared Tendler
Jared Tendler is an internationally recognized mental game coach. His clients include world champion poker players, the #1 ranked pool player in the world, professional golfers, and more recently, traders too.
If Jared was to summarize exactly what he does (and what he specializes in), it would be; removing negative and excessive emotion from decision making.
So naturally, this serves as the underlying theme throughout our conversation, but we also discuss higher-level topics like; variance, the major crossovers between high-stakes poker and trading, how psychology has been oversold and when it really matters, plus how to identify various types of “tilt.”
- - - - - -
LINKS
- - - - - -
· More interviews: https://chatwithtraders.com
· Free resources: https://chatwithtraders.com/resources
· Sponsored by Technician: http://technicianapp.com/
· Twitter: https://twitter.com/chatwithtraders
· Facebook: http://facebook.com/chatwithtraders
· Instagram: https://instagram.com/chatwithtraders_
· Soundcloud: https://soundcloud.com/chat-with-traders
· Stitcher: http://www.stitcher.com/podcast/chat-with-traders

published:21 Aug 2016

views:21805

The ability to computationally solve imperfect-information games has a myriad of future applications ranging from auctions, negotiations, and (cyber)security settings to medical domains. A dramatic scalability leap has occurred in the capability to solve such games over the last nine years, fueled in large part by the AnnualComputerPokerCompetition. I will discuss the key, domain-independent, techniques that enabled this leap, including automated abstraction techniques and approaches for mitigating the issues that they raise, new equilibrium-finding algorithms, safe opponent exploitation methods, techniques that use qualitative knowledge as an extra input, and endgame solving techniques. I will also include new results on 1) developing the world’s best Heads-Up No-Limit Texas Hold'em poker program, 2) theory that enables abstraction that gives solution quality guarantees, 3) techniques for hot starting equilibrium finding, 4) simultaneous abstraction and equilibrium finding, and 5) theory that improves gradient-based equilibrium finding. I will also cover the Brains vs AI competition that I recently organized where our AI, Claudico, challenged four of the top-10 human pros in Heads-Up No-Limit Texas Hold'em for 80,000 hands. (The talk covers joint work with many co-authors, mostly NoamBrown, Sam Ganzfried, and Christian Kroer.

published:22 Jun 2016

views:2648

EP 090: This quants’ approach to designing algo strategies—Michael Halls-Moore, of QuantStart
For this episode I’m joined by Michael Halls-Moore, who runs QuantStart.com—a site well-known by many algorithmic traders.
Prior to trading, Michael studied computational fluid dynamics and was the co-founder of a tech startup, before getting involved a small equity fund as a quant developer—where his key role was cleansing data.
Now, independently, Michael trades his own short-term algorithmic strategies, consults to hedge funds on machine learning and quant infrastructure, and also has a keen interest in space exploration.
We discussed a whole range of topics, including; the need for quality data, thinking about risk from a portfolio level, trading multiple automated strategies, the role of common sense in parameter optimization, learning to program, and more.
- - - - - -
LINKS
- - - - - -
· Show notes: https://chatwithtraders.com/ep-090-michael-halls-moore/
· More interviews: https://chatwithtraders.com
· Free resources: https://chatwithtraders.com/resources
· Sponsored by Tradovate: https://tradovate.com/traders
· Twitter: https://twitter.com/chatwithtraders
· Facebook: http://facebook.com/chatwithtraders
· Instagram: https://instagram.com/chatwithtraders_
· Soundcloud: https://soundcloud.com/chat-with-traders
· Stitcher: http://www.stitcher.com/podcast/chat-with-traders

When Siri helped a young criminal nearly get away with murder, future crimes expert Marc Goodman realized how algorithms had become co-conspirators in a new age of digital crime. Goodman's latest book is "FutureCrimes: Inside the Digital Underground and the Battle for Our ConnectedWorld" (http://goo.gl/tw9EIi).
Read more at BigThink.com: http://bigthink.com/videos/marc-goodman-on-artificial-intelligence-and-the-future-of-crime
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Transcript - There was a case recently in Florida where a teenager was arrested for murder. He was a student at the University of Florida and he was accused of murdering his roommate and best friend. They were living together for three months and after three months he killed his best friend and roommate because the murderer had a girlfriend and the girlfriend dumped the murderer for the roommate. As it turns out when the kid murdered his roommate he had a problem. He didn’t know where to bury the dead body. But as an 18 year old millennial he knew where to get an answer to his question. He asked Siri where can I bury a dead body. And it turns out Siri answered his question and proposed minds, dumps, reservoirs, swamps and rivers. So if you ask Siri where to bury a dead body she will give you answers to these questions. So in the old days, you know, we used to talk about Bonnie and Clyde. But we’ve entered the age of Siri and Clyde where clearly algorithms will be unnamed co-conspirators in younger criminals carrying out attacks.
AI is being used across the board in our society via algorithms, right. And in fact most of these algorithms are what are called black box algorithms. There’s a HarvardProfessor called FrankPasquale who wrote a whole book on black box algorithms that talks about how little we know about these algorithms and what some of the dangers might be. For example there’s an algorithm that determines your credit score, FICO. What goes into it exactly and precisely nobody knows. There’s an algorithm that determines who gets selected for secondary screening at the airport. Maybe it’s because you bought a one way ticket? Maybe it’s because you paid in cash? Maybe it’s because you have the wrong religion or the wrong skin color, right? We don’t know clearly at all what is being encoded into these algorithms. And so that opens up the door for them to be abused. We saw examples of this on Wall Street with Flash Boys, right, on rapid trading on Wall Street. The fact of the matter is only a miniscule amount of trading on Wall Street is carried out by human beings. The overwhelming majority of trading is algorithmic in nature. It’s preprogrammed so that if the price of soybeans goes down all these additional steps will take place. If some event happens in the world traders will buy or sell based upon that information. And it all goes so fast there’s no time for human intervention. And we saw an implication of this recently when the Associated Press Twitter feed was hacked a few years ago. Somebody took over the official AP Twitter feed and they put out a tweet from the official site that said breaking news, explosions at the White House, President Obama injured. Now it turns out that never happened but all the algorithms that are monitoring the Net looking for the latest news that they can trade on picked up on it immediately from a trusted source. And because they perceived a terrorist attack that caused the market a massive, massive sell off. In just three minutes because of this one tweet the market fell 136 billion dollars, 136 billion dollars of valuation was evaporated in 180 seconds just because of one wayward tweet. And that was carried out by an organization known as the Syrian Electronic Army. They’re backed, trained and funded by the Iranian government. Now in this particular case they did that for the purposes of mischief. But they also could have shorted the market at the same time and made a lot of money on this. So our algorithms are going way faster than we realize and they’re running things that we don’t even understand. For example when you go for an MRI exam in the hospital that MRI via its algorithms are actually interpreting the data for your radiologist in many ways before they even read that. When you fly on autopilot on an airplane which is probably more than 90 percent of your flight it’s an algorithm that’s running the plane. And all of those can be hacked. Much of the attacks that occur in cyberspace whether they be virus attacks, ransomware, denial of services attacks are all scripted to run which means a computer hacker or criminal writes some code and the code goes off and carries out the crime. Which means crime can scale and it can scale exponentially. And that’s why we’ve seen some massive uptick.

Deep Algo Tutorial #1 - Poker Game Algorithms

WARNING : This video is not up to date!
If you want to see the last version of DeepAlgo live : ask a free demo on DeepAlgo.com
Deep Algo is a SaaS platform based on 100% automatic algorithm extraction technology.
This Tutorial explains how you can Understand the source code of a poker game.

57:33

How Science is Taking the Luck out of Gambling - with Adam Kucharski

How Science is Taking the Luck out of Gambling - with Adam Kucharski

How Science is Taking the Luck out of Gambling - with Adam Kucharski

From the statisticians forecasting sports scores to the intelligent bots beating human poker players, Adam Kucharski traces the scientific origins of the world's best gambling strategies.
Watch the Q&A here: https://www.youtube.com/watch?v=o0XxbHnf5ro
Subscribe for regular science videos: http://bit.ly/RiSubscRibe
Spanning mathematics, psychology, economics and physics, Adam Kucharski reveals the long and tangled history between betting and science, and explains how gambling shaped everything from probability to game theory, and chaos theory to artificial intelligence.
Adam Kucharski is a Lecturer at LondonSchool of Hygiene and Tropical Medicine where his research focusses on the dynamics of infectious diseases, particularly emerging infections. Prior to this, he got a degree in mathematics from the University of Warwick, received a PhD in applied mathematics from the University of Cambridge and had a post-doc position at Imperial College London.
The Ri is on Twitter: http://twitter.com/ri_science
and Facebook: http://www.facebook.com/royalinstitution
and Tumblr: http://ri-science.tumblr.com/
Our editorial policy: http://www.rigb.org/home/editorial-po...
Subscribe for the latest science videos: http://bit.ly/RiNewsletter

Mental game lessons, from world champion poker coach—Jared Tendler

EP 086: What traders can learn about mental game, from world champion poker coach—Jared Tendler
Jared Tendler is an internationally recognized mental game coach. His clients include world champion poker players, the #1 ranked pool player in the world, professional golfers, and more recently, traders too.
If Jared was to summarize exactly what he does (and what he specializes in), it would be; removing negative and excessive emotion from decision making.
So naturally, this serves as the underlying theme throughout our conversation, but we also discuss higher-level topics like; variance, the major crossovers between high-stakes poker and trading, how psychology has been oversold and when it really matters, plus how to identify various types of “tilt.”
- - - - - -
LINKS
- - - - - -
· More interviews: https://chatwithtraders.com
· Free resources: https://chatwithtraders.com/resources
· Sponsored by Technician: http://technicianapp.com/
· Twitter: https://twitter.com/chatwithtraders
· Facebook: http://facebook.com/chatwithtraders
· Instagram: https://instagram.com/chatwithtraders_
· Soundcloud: https://soundcloud.com/chat-with-traders
· Stitcher: http://www.stitcher.com/podcast/chat-with-traders

1:30:10

The State of Techniques for Solving Large Imperfect-Information Games, Including Poker

The State of Techniques for Solving Large Imperfect-Information Games, Including Poker

The State of Techniques for Solving Large Imperfect-Information Games, Including Poker

The ability to computationally solve imperfect-information games has a myriad of future applications ranging from auctions, negotiations, and (cyber)security settings to medical domains. A dramatic scalability leap has occurred in the capability to solve such games over the last nine years, fueled in large part by the AnnualComputerPokerCompetition. I will discuss the key, domain-independent, techniques that enabled this leap, including automated abstraction techniques and approaches for mitigating the issues that they raise, new equilibrium-finding algorithms, safe opponent exploitation methods, techniques that use qualitative knowledge as an extra input, and endgame solving techniques. I will also include new results on 1) developing the world’s best Heads-Up No-Limit Texas Hold'em poker program, 2) theory that enables abstraction that gives solution quality guarantees, 3) techniques for hot starting equilibrium finding, 4) simultaneous abstraction and equilibrium finding, and 5) theory that improves gradient-based equilibrium finding. I will also cover the Brains vs AI competition that I recently organized where our AI, Claudico, challenged four of the top-10 human pros in Heads-Up No-Limit Texas Hold'em for 80,000 hands. (The talk covers joint work with many co-authors, mostly NoamBrown, Sam Ganzfried, and Christian Kroer.

How ‘Black Box’ Algorithms are Assisting a New Generation of Criminals

How ‘Black Box’ Algorithms are Assisting a New Generation of Criminals

How ‘Black Box’ Algorithms are Assisting a New Generation of Criminals

When Siri helped a young criminal nearly get away with murder, future crimes expert Marc Goodman realized how algorithms had become co-conspirators in a new age of digital crime. Goodman's latest book is "FutureCrimes: Inside the Digital Underground and the Battle for Our ConnectedWorld" (http://goo.gl/tw9EIi).
Read more at BigThink.com: http://bigthink.com/videos/marc-goodman-on-artificial-intelligence-and-the-future-of-crime
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Transcript - There was a case recently in Florida where a teenager was arrested for murder. He was a student at the University of Florida and he was accused of murdering his roommate and best friend. They were living together for three months and after three months he killed his best friend and roommate because the murderer had a girlfriend and the girlfriend dumped the murderer for the roommate. As it turns out when the kid murdered his roommate he had a problem. He didn’t know where to bury the dead body. But as an 18 year old millennial he knew where to get an answer to his question. He asked Siri where can I bury a dead body. And it turns out Siri answered his question and proposed minds, dumps, reservoirs, swamps and rivers. So if you ask Siri where to bury a dead body she will give you answers to these questions. So in the old days, you know, we used to talk about Bonnie and Clyde. But we’ve entered the age of Siri and Clyde where clearly algorithms will be unnamed co-conspirators in younger criminals carrying out attacks.
AI is being used across the board in our society via algorithms, right. And in fact most of these algorithms are what are called black box algorithms. There’s a HarvardProfessor called FrankPasquale who wrote a whole book on black box algorithms that talks about how little we know about these algorithms and what some of the dangers might be. For example there’s an algorithm that determines your credit score, FICO. What goes into it exactly and precisely nobody knows. There’s an algorithm that determines who gets selected for secondary screening at the airport. Maybe it’s because you bought a one way ticket? Maybe it’s because you paid in cash? Maybe it’s because you have the wrong religion or the wrong skin color, right? We don’t know clearly at all what is being encoded into these algorithms. And so that opens up the door for them to be abused. We saw examples of this on Wall Street with Flash Boys, right, on rapid trading on Wall Street. The fact of the matter is only a miniscule amount of trading on Wall Street is carried out by human beings. The overwhelming majority of trading is algorithmic in nature. It’s preprogrammed so that if the price of soybeans goes down all these additional steps will take place. If some event happens in the world traders will buy or sell based upon that information. And it all goes so fast there’s no time for human intervention. And we saw an implication of this recently when the Associated Press Twitter feed was hacked a few years ago. Somebody took over the official AP Twitter feed and they put out a tweet from the official site that said breaking news, explosions at the White House, President Obama injured. Now it turns out that never happened but all the algorithms that are monitoring the Net looking for the latest news that they can trade on picked up on it immediately from a trusted source. And because they perceived a terrorist attack that caused the market a massive, massive sell off. In just three minutes because of this one tweet the market fell 136 billion dollars, 136 billion dollars of valuation was evaporated in 180 seconds just because of one wayward tweet. And that was carried out by an organization known as the Syrian Electronic Army. They’re backed, trained and funded by the Iranian government. Now in this particular case they did that for the purposes of mischief. But they also could have shorted the market at the same time and made a lot of money on this. So our algorithms are going way faster than we realize and they’re running things that we don’t even understand. For example when you go for an MRI exam in the hospital that MRI via its algorithms are actually interpreting the data for your radiologist in many ways before they even read that. When you fly on autopilot on an airplane which is probably more than 90 percent of your flight it’s an algorithm that’s running the plane. And all of those can be hacked. Much of the attacks that occur in cyberspace whether they be virus attacks, ransomware, denial of services attacks are all scripted to run which means a computer hacker or criminal writes some code and the code goes off and carries out the crime. Which means crime can scale and it can scale exponentially. And that’s why we’ve seen some massive uptick.

Intro to Game Theory and the Dominant Strategy Equilibrium

http://economicsdetective.com/
Game theory is the study of human behaviour in strategic settings. It is used to solve some of the harder problems in economics.
So what is a game? To have a game, you need at least two players, sometimes called agents, or, if you want to be really crazy, people. And you need payoffs for the players, you need to define the outcomes they can potentially get depending on how the game unfolds. And finally, you need rules for the game.
Now, it's not always obvious how people will behave, even with players, payoffs, and rules clearly defined. That's why game theorists have a number of solution concepts for games, including the dominant strategy equilibrium, the Nash equilibrium, the subgame perfect Nash equilibrium, the Bayesian equilibrium, and the weak perfect Bayesian equilibrium.
The most basic solution concept is the dominant strategy equilibrium. In a game, each player can have any number of possible strategies. One strategy strictly dominates another strategy if the player is always better off under that strategy no matter what other players do. If one strategy strictly dominates every other possible strategy a player could take, that strategy is a strictly dominant strategy. We have a dominant strategy equilibrium when all players play a strictly dominant strategy.
Now let's look at the most famous game in game theory, the Prisoner's Dilemma. There are two prisoners, prisoner 1 and prisoner 2, and they each have a choice. They can testify against the other, or they can keep quiet.
If they both keep quiet, they both get off with a light sentence, which I'll represent with a payoff of 2. Prisoner 1's payoff is on the left, prisoner 2's is on the right. If they both testify, they both get a moderate sentence. I'll represent the moderate sentence by a payoff of 0. Right about now, keeping quiet is looking like the best option, but there's more to this game. If one testifies and the other keeps quiet, the one who testified will get off scot free, and the one who kept quiet will get an extremely harsh sentence; they'll throw the book at him.
Think about this game for a moment. Keeping quiet looks like a pretty good option if both prisoners could promise not to testify. But these prisoners only care about their own self-interest. So, both prisoners may tell the other they pinky swear not to testify, but they won't keep that promise. If prisoner 2 keeps quiet, prisoner 1 is better off testifying. If prisoner 2 testifies, prisoner 1 is better off testifying. Testifying is a dominant strategy for both players, so both testifying is the dominant strategy equilibrium.
The prisoner's dilemma comes up in all sorts of situations. For instance, instead of prisoners our players could be, say, oil companies. If both set a high price they can sell for a high price, but each one has an incentive to undercut, in which case he will capture the entire market. The equilibrium outcome is for each company to charge a low price.
The prisoner's dilemma isn't the only game with a dominant strategy equilibrium. Here's a more complicated one. Can you tell which strategy is dominant? It's A for player 1, and E for player 2. So the dominant strategy equilibrium is A, E.

Deep Algo Tutorial #1 - Poker Game Algorithms

WARNING : This video is not up to date!
If you want to see the last version of DeepAlgo live : ask a free demo on DeepAlgo.com
Deep Algo is a SaaS platform based on 100% automatic algorithm extraction technology.
This Tutorial explains how you can Understand the source code of a poker game.

published: 22 Jul 2016

How Science is Taking the Luck out of Gambling - with Adam Kucharski

From the statisticians forecasting sports scores to the intelligent bots beating human poker players, Adam Kucharski traces the scientific origins of the world's best gambling strategies.
Watch the Q&A here: https://www.youtube.com/watch?v=o0XxbHnf5ro
Subscribe for regular science videos: http://bit.ly/RiSubscRibe
Spanning mathematics, psychology, economics and physics, Adam Kucharski reveals the long and tangled history between betting and science, and explains how gambling shaped everything from probability to game theory, and chaos theory to artificial intelligence.
Adam Kucharski is a Lecturer at LondonSchool of Hygiene and Tropical Medicine where his research focusses on the dynamics of infectious diseases, particularly emerging infections. Prior to this, he got a degree in mathema...

K-means Algorithm Demo

Mental game lessons, from world champion poker coach—Jared Tendler

EP 086: What traders can learn about mental game, from world champion poker coach—Jared Tendler
Jared Tendler is an internationally recognized mental game coach. His clients include world champion poker players, the #1 ranked pool player in the world, professional golfers, and more recently, traders too.
If Jared was to summarize exactly what he does (and what he specializes in), it would be; removing negative and excessive emotion from decision making.
So naturally, this serves as the underlying theme throughout our conversation, but we also discuss higher-level topics like; variance, the major crossovers between high-stakes poker and trading, how psychology has been oversold and when it really matters, plus how to identify various types of “tilt.”
- - - - - -
LINKS
- - - - - -
· Mor...

published: 21 Aug 2016

The State of Techniques for Solving Large Imperfect-Information Games, Including Poker

The ability to computationally solve imperfect-information games has a myriad of future applications ranging from auctions, negotiations, and (cyber)security settings to medical domains. A dramatic scalability leap has occurred in the capability to solve such games over the last nine years, fueled in large part by the AnnualComputerPokerCompetition. I will discuss the key, domain-independent, techniques that enabled this leap, including automated abstraction techniques and approaches for mitigating the issues that they raise, new equilibrium-finding algorithms, safe opponent exploitation methods, techniques that use qualitative knowledge as an extra input, and endgame solving techniques. I will also include new results on 1) developing the world’s best Heads-Up No-Limit Texas Hold'em po...

EP 090: This quants’ approach to designing algo strategies—Michael Halls-Moore, of QuantStart
For this episode I’m joined by Michael Halls-Moore, who runs QuantStart.com—a site well-known by many algorithmic traders.
Prior to trading, Michael studied computational fluid dynamics and was the co-founder of a tech startup, before getting involved a small equity fund as a quant developer—where his key role was cleansing data.
Now, independently, Michael trades his own short-term algorithmic strategies, consults to hedge funds on machine learning and quant infrastructure, and also has a keen interest in space exploration.
We discussed a whole range of topics, including; the need for quality data, thinking about risk from a portfolio level, trading multiple automated strategies, the role of ...

How ‘Black Box’ Algorithms are Assisting a New Generation of Criminals

When Siri helped a young criminal nearly get away with murder, future crimes expert Marc Goodman realized how algorithms had become co-conspirators in a new age of digital crime. Goodman's latest book is "FutureCrimes: Inside the Digital Underground and the Battle for Our ConnectedWorld" (http://goo.gl/tw9EIi).
Read more at BigThink.com: http://bigthink.com/videos/marc-goodman-on-artificial-intelligence-and-the-future-of-crime
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Transcript - There was a case recently in Florida where a teenager was arrested for murder. He was a student at the University of Florida and he was accused of murdering his roommate and best friend. They were living toget...

Intro to Game Theory and the Dominant Strategy Equilibrium

http://economicsdetective.com/
Game theory is the study of human behaviour in strategic settings. It is used to solve some of the harder problems in economics.
So what is a game? To have a game, you need at least two players, sometimes called agents, or, if you want to be really crazy, people. And you need payoffs for the players, you need to define the outcomes they can potentially get depending on how the game unfolds. And finally, you need rules for the game.
Now, it's not always obvious how people will behave, even with players, payoffs, and rules clearly defined. That's why game theorists have a number of solution concepts for games, including the dominant strategy equilibrium, the Nash equilibrium, the subgame perfect Nash equilibrium, the Bayesian equilibrium, and the weak p...

Deep Algo Tutorial #1 - Poker Game Algorithms

WARNING : This video is not up to date!
If you want to see the last version of DeepAlgo live : ask a free demo on DeepAlgo.com
Deep Algo is a SaaS platform b...

WARNING : This video is not up to date!
If you want to see the last version of DeepAlgo live : ask a free demo on DeepAlgo.com
Deep Algo is a SaaS platform based on 100% automatic algorithm extraction technology.
This Tutorial explains how you can Understand the source code of a poker game.

WARNING : This video is not up to date!
If you want to see the last version of DeepAlgo live : ask a free demo on DeepAlgo.com
Deep Algo is a SaaS platform based on 100% automatic algorithm extraction technology.
This Tutorial explains how you can Understand the source code of a poker game.

How Science is Taking the Luck out of Gambling - with Adam Kucharski

From the statisticians forecasting sports scores to the intelligent bots beating human poker players, Adam Kucharski traces the scientific origins of the world'...

From the statisticians forecasting sports scores to the intelligent bots beating human poker players, Adam Kucharski traces the scientific origins of the world's best gambling strategies.
Watch the Q&A here: https://www.youtube.com/watch?v=o0XxbHnf5ro
Subscribe for regular science videos: http://bit.ly/RiSubscRibe
Spanning mathematics, psychology, economics and physics, Adam Kucharski reveals the long and tangled history between betting and science, and explains how gambling shaped everything from probability to game theory, and chaos theory to artificial intelligence.
Adam Kucharski is a Lecturer at LondonSchool of Hygiene and Tropical Medicine where his research focusses on the dynamics of infectious diseases, particularly emerging infections. Prior to this, he got a degree in mathematics from the University of Warwick, received a PhD in applied mathematics from the University of Cambridge and had a post-doc position at Imperial College London.
The Ri is on Twitter: http://twitter.com/ri_science
and Facebook: http://www.facebook.com/royalinstitution
and Tumblr: http://ri-science.tumblr.com/
Our editorial policy: http://www.rigb.org/home/editorial-po...
Subscribe for the latest science videos: http://bit.ly/RiNewsletter

From the statisticians forecasting sports scores to the intelligent bots beating human poker players, Adam Kucharski traces the scientific origins of the world's best gambling strategies.
Watch the Q&A here: https://www.youtube.com/watch?v=o0XxbHnf5ro
Subscribe for regular science videos: http://bit.ly/RiSubscRibe
Spanning mathematics, psychology, economics and physics, Adam Kucharski reveals the long and tangled history between betting and science, and explains how gambling shaped everything from probability to game theory, and chaos theory to artificial intelligence.
Adam Kucharski is a Lecturer at LondonSchool of Hygiene and Tropical Medicine where his research focusses on the dynamics of infectious diseases, particularly emerging infections. Prior to this, he got a degree in mathematics from the University of Warwick, received a PhD in applied mathematics from the University of Cambridge and had a post-doc position at Imperial College London.
The Ri is on Twitter: http://twitter.com/ri_science
and Facebook: http://www.facebook.com/royalinstitution
and Tumblr: http://ri-science.tumblr.com/
Our editorial policy: http://www.rigb.org/home/editorial-po...
Subscribe for the latest science videos: http://bit.ly/RiNewsletter

Mental game lessons, from world champion poker coach—Jared Tendler

EP 086: What traders can learn about mental game, from world champion poker coach—Jared Tendler
Jared Tendler is an internationally recognized mental game coac...

EP 086: What traders can learn about mental game, from world champion poker coach—Jared Tendler
Jared Tendler is an internationally recognized mental game coach. His clients include world champion poker players, the #1 ranked pool player in the world, professional golfers, and more recently, traders too.
If Jared was to summarize exactly what he does (and what he specializes in), it would be; removing negative and excessive emotion from decision making.
So naturally, this serves as the underlying theme throughout our conversation, but we also discuss higher-level topics like; variance, the major crossovers between high-stakes poker and trading, how psychology has been oversold and when it really matters, plus how to identify various types of “tilt.”
- - - - - -
LINKS
- - - - - -
· More interviews: https://chatwithtraders.com
· Free resources: https://chatwithtraders.com/resources
· Sponsored by Technician: http://technicianapp.com/
· Twitter: https://twitter.com/chatwithtraders
· Facebook: http://facebook.com/chatwithtraders
· Instagram: https://instagram.com/chatwithtraders_
· Soundcloud: https://soundcloud.com/chat-with-traders
· Stitcher: http://www.stitcher.com/podcast/chat-with-traders

EP 086: What traders can learn about mental game, from world champion poker coach—Jared Tendler
Jared Tendler is an internationally recognized mental game coach. His clients include world champion poker players, the #1 ranked pool player in the world, professional golfers, and more recently, traders too.
If Jared was to summarize exactly what he does (and what he specializes in), it would be; removing negative and excessive emotion from decision making.
So naturally, this serves as the underlying theme throughout our conversation, but we also discuss higher-level topics like; variance, the major crossovers between high-stakes poker and trading, how psychology has been oversold and when it really matters, plus how to identify various types of “tilt.”
- - - - - -
LINKS
- - - - - -
· More interviews: https://chatwithtraders.com
· Free resources: https://chatwithtraders.com/resources
· Sponsored by Technician: http://technicianapp.com/
· Twitter: https://twitter.com/chatwithtraders
· Facebook: http://facebook.com/chatwithtraders
· Instagram: https://instagram.com/chatwithtraders_
· Soundcloud: https://soundcloud.com/chat-with-traders
· Stitcher: http://www.stitcher.com/podcast/chat-with-traders

published:21 Aug 2016

views:21805

back

The State of Techniques for Solving Large Imperfect-Information Games, Including Poker

The ability to computationally solve imperfect-information games has a myriad of future applications ranging from auctions, negotiations, and (cyber)security se...

The ability to computationally solve imperfect-information games has a myriad of future applications ranging from auctions, negotiations, and (cyber)security settings to medical domains. A dramatic scalability leap has occurred in the capability to solve such games over the last nine years, fueled in large part by the AnnualComputerPokerCompetition. I will discuss the key, domain-independent, techniques that enabled this leap, including automated abstraction techniques and approaches for mitigating the issues that they raise, new equilibrium-finding algorithms, safe opponent exploitation methods, techniques that use qualitative knowledge as an extra input, and endgame solving techniques. I will also include new results on 1) developing the world’s best Heads-Up No-Limit Texas Hold'em poker program, 2) theory that enables abstraction that gives solution quality guarantees, 3) techniques for hot starting equilibrium finding, 4) simultaneous abstraction and equilibrium finding, and 5) theory that improves gradient-based equilibrium finding. I will also cover the Brains vs AI competition that I recently organized where our AI, Claudico, challenged four of the top-10 human pros in Heads-Up No-Limit Texas Hold'em for 80,000 hands. (The talk covers joint work with many co-authors, mostly NoamBrown, Sam Ganzfried, and Christian Kroer.

The ability to computationally solve imperfect-information games has a myriad of future applications ranging from auctions, negotiations, and (cyber)security settings to medical domains. A dramatic scalability leap has occurred in the capability to solve such games over the last nine years, fueled in large part by the AnnualComputerPokerCompetition. I will discuss the key, domain-independent, techniques that enabled this leap, including automated abstraction techniques and approaches for mitigating the issues that they raise, new equilibrium-finding algorithms, safe opponent exploitation methods, techniques that use qualitative knowledge as an extra input, and endgame solving techniques. I will also include new results on 1) developing the world’s best Heads-Up No-Limit Texas Hold'em poker program, 2) theory that enables abstraction that gives solution quality guarantees, 3) techniques for hot starting equilibrium finding, 4) simultaneous abstraction and equilibrium finding, and 5) theory that improves gradient-based equilibrium finding. I will also cover the Brains vs AI competition that I recently organized where our AI, Claudico, challenged four of the top-10 human pros in Heads-Up No-Limit Texas Hold'em for 80,000 hands. (The talk covers joint work with many co-authors, mostly NoamBrown, Sam Ganzfried, and Christian Kroer.

How ‘Black Box’ Algorithms are Assisting a New Generation of Criminals

When Siri helped a young criminal nearly get away with murder, future crimes expert Marc Goodman realized how algorithms had become co-conspirators in a new age...

When Siri helped a young criminal nearly get away with murder, future crimes expert Marc Goodman realized how algorithms had become co-conspirators in a new age of digital crime. Goodman's latest book is "FutureCrimes: Inside the Digital Underground and the Battle for Our ConnectedWorld" (http://goo.gl/tw9EIi).
Read more at BigThink.com: http://bigthink.com/videos/marc-goodman-on-artificial-intelligence-and-the-future-of-crime
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Transcript - There was a case recently in Florida where a teenager was arrested for murder. He was a student at the University of Florida and he was accused of murdering his roommate and best friend. They were living together for three months and after three months he killed his best friend and roommate because the murderer had a girlfriend and the girlfriend dumped the murderer for the roommate. As it turns out when the kid murdered his roommate he had a problem. He didn’t know where to bury the dead body. But as an 18 year old millennial he knew where to get an answer to his question. He asked Siri where can I bury a dead body. And it turns out Siri answered his question and proposed minds, dumps, reservoirs, swamps and rivers. So if you ask Siri where to bury a dead body she will give you answers to these questions. So in the old days, you know, we used to talk about Bonnie and Clyde. But we’ve entered the age of Siri and Clyde where clearly algorithms will be unnamed co-conspirators in younger criminals carrying out attacks.
AI is being used across the board in our society via algorithms, right. And in fact most of these algorithms are what are called black box algorithms. There’s a HarvardProfessor called FrankPasquale who wrote a whole book on black box algorithms that talks about how little we know about these algorithms and what some of the dangers might be. For example there’s an algorithm that determines your credit score, FICO. What goes into it exactly and precisely nobody knows. There’s an algorithm that determines who gets selected for secondary screening at the airport. Maybe it’s because you bought a one way ticket? Maybe it’s because you paid in cash? Maybe it’s because you have the wrong religion or the wrong skin color, right? We don’t know clearly at all what is being encoded into these algorithms. And so that opens up the door for them to be abused. We saw examples of this on Wall Street with Flash Boys, right, on rapid trading on Wall Street. The fact of the matter is only a miniscule amount of trading on Wall Street is carried out by human beings. The overwhelming majority of trading is algorithmic in nature. It’s preprogrammed so that if the price of soybeans goes down all these additional steps will take place. If some event happens in the world traders will buy or sell based upon that information. And it all goes so fast there’s no time for human intervention. And we saw an implication of this recently when the Associated Press Twitter feed was hacked a few years ago. Somebody took over the official AP Twitter feed and they put out a tweet from the official site that said breaking news, explosions at the White House, President Obama injured. Now it turns out that never happened but all the algorithms that are monitoring the Net looking for the latest news that they can trade on picked up on it immediately from a trusted source. And because they perceived a terrorist attack that caused the market a massive, massive sell off. In just three minutes because of this one tweet the market fell 136 billion dollars, 136 billion dollars of valuation was evaporated in 180 seconds just because of one wayward tweet. And that was carried out by an organization known as the Syrian Electronic Army. They’re backed, trained and funded by the Iranian government. Now in this particular case they did that for the purposes of mischief. But they also could have shorted the market at the same time and made a lot of money on this. So our algorithms are going way faster than we realize and they’re running things that we don’t even understand. For example when you go for an MRI exam in the hospital that MRI via its algorithms are actually interpreting the data for your radiologist in many ways before they even read that. When you fly on autopilot on an airplane which is probably more than 90 percent of your flight it’s an algorithm that’s running the plane. And all of those can be hacked. Much of the attacks that occur in cyberspace whether they be virus attacks, ransomware, denial of services attacks are all scripted to run which means a computer hacker or criminal writes some code and the code goes off and carries out the crime. Which means crime can scale and it can scale exponentially. And that’s why we’ve seen some massive uptick.

When Siri helped a young criminal nearly get away with murder, future crimes expert Marc Goodman realized how algorithms had become co-conspirators in a new age of digital crime. Goodman's latest book is "FutureCrimes: Inside the Digital Underground and the Battle for Our ConnectedWorld" (http://goo.gl/tw9EIi).
Read more at BigThink.com: http://bigthink.com/videos/marc-goodman-on-artificial-intelligence-and-the-future-of-crime
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Transcript - There was a case recently in Florida where a teenager was arrested for murder. He was a student at the University of Florida and he was accused of murdering his roommate and best friend. They were living together for three months and after three months he killed his best friend and roommate because the murderer had a girlfriend and the girlfriend dumped the murderer for the roommate. As it turns out when the kid murdered his roommate he had a problem. He didn’t know where to bury the dead body. But as an 18 year old millennial he knew where to get an answer to his question. He asked Siri where can I bury a dead body. And it turns out Siri answered his question and proposed minds, dumps, reservoirs, swamps and rivers. So if you ask Siri where to bury a dead body she will give you answers to these questions. So in the old days, you know, we used to talk about Bonnie and Clyde. But we’ve entered the age of Siri and Clyde where clearly algorithms will be unnamed co-conspirators in younger criminals carrying out attacks.
AI is being used across the board in our society via algorithms, right. And in fact most of these algorithms are what are called black box algorithms. There’s a HarvardProfessor called FrankPasquale who wrote a whole book on black box algorithms that talks about how little we know about these algorithms and what some of the dangers might be. For example there’s an algorithm that determines your credit score, FICO. What goes into it exactly and precisely nobody knows. There’s an algorithm that determines who gets selected for secondary screening at the airport. Maybe it’s because you bought a one way ticket? Maybe it’s because you paid in cash? Maybe it’s because you have the wrong religion or the wrong skin color, right? We don’t know clearly at all what is being encoded into these algorithms. And so that opens up the door for them to be abused. We saw examples of this on Wall Street with Flash Boys, right, on rapid trading on Wall Street. The fact of the matter is only a miniscule amount of trading on Wall Street is carried out by human beings. The overwhelming majority of trading is algorithmic in nature. It’s preprogrammed so that if the price of soybeans goes down all these additional steps will take place. If some event happens in the world traders will buy or sell based upon that information. And it all goes so fast there’s no time for human intervention. And we saw an implication of this recently when the Associated Press Twitter feed was hacked a few years ago. Somebody took over the official AP Twitter feed and they put out a tweet from the official site that said breaking news, explosions at the White House, President Obama injured. Now it turns out that never happened but all the algorithms that are monitoring the Net looking for the latest news that they can trade on picked up on it immediately from a trusted source. And because they perceived a terrorist attack that caused the market a massive, massive sell off. In just three minutes because of this one tweet the market fell 136 billion dollars, 136 billion dollars of valuation was evaporated in 180 seconds just because of one wayward tweet. And that was carried out by an organization known as the Syrian Electronic Army. They’re backed, trained and funded by the Iranian government. Now in this particular case they did that for the purposes of mischief. But they also could have shorted the market at the same time and made a lot of money on this. So our algorithms are going way faster than we realize and they’re running things that we don’t even understand. For example when you go for an MRI exam in the hospital that MRI via its algorithms are actually interpreting the data for your radiologist in many ways before they even read that. When you fly on autopilot on an airplane which is probably more than 90 percent of your flight it’s an algorithm that’s running the plane. And all of those can be hacked. Much of the attacks that occur in cyberspace whether they be virus attacks, ransomware, denial of services attacks are all scripted to run which means a computer hacker or criminal writes some code and the code goes off and carries out the crime. Which means crime can scale and it can scale exponentially. And that’s why we’ve seen some massive uptick.

Intro to Game Theory and the Dominant Strategy Equilibrium

http://economicsdetective.com/
Game theory is the study of human behaviour in strategic settings. It is used to solve some of the harder problems in economics...

http://economicsdetective.com/
Game theory is the study of human behaviour in strategic settings. It is used to solve some of the harder problems in economics.
So what is a game? To have a game, you need at least two players, sometimes called agents, or, if you want to be really crazy, people. And you need payoffs for the players, you need to define the outcomes they can potentially get depending on how the game unfolds. And finally, you need rules for the game.
Now, it's not always obvious how people will behave, even with players, payoffs, and rules clearly defined. That's why game theorists have a number of solution concepts for games, including the dominant strategy equilibrium, the Nash equilibrium, the subgame perfect Nash equilibrium, the Bayesian equilibrium, and the weak perfect Bayesian equilibrium.
The most basic solution concept is the dominant strategy equilibrium. In a game, each player can have any number of possible strategies. One strategy strictly dominates another strategy if the player is always better off under that strategy no matter what other players do. If one strategy strictly dominates every other possible strategy a player could take, that strategy is a strictly dominant strategy. We have a dominant strategy equilibrium when all players play a strictly dominant strategy.
Now let's look at the most famous game in game theory, the Prisoner's Dilemma. There are two prisoners, prisoner 1 and prisoner 2, and they each have a choice. They can testify against the other, or they can keep quiet.
If they both keep quiet, they both get off with a light sentence, which I'll represent with a payoff of 2. Prisoner 1's payoff is on the left, prisoner 2's is on the right. If they both testify, they both get a moderate sentence. I'll represent the moderate sentence by a payoff of 0. Right about now, keeping quiet is looking like the best option, but there's more to this game. If one testifies and the other keeps quiet, the one who testified will get off scot free, and the one who kept quiet will get an extremely harsh sentence; they'll throw the book at him.
Think about this game for a moment. Keeping quiet looks like a pretty good option if both prisoners could promise not to testify. But these prisoners only care about their own self-interest. So, both prisoners may tell the other they pinky swear not to testify, but they won't keep that promise. If prisoner 2 keeps quiet, prisoner 1 is better off testifying. If prisoner 2 testifies, prisoner 1 is better off testifying. Testifying is a dominant strategy for both players, so both testifying is the dominant strategy equilibrium.
The prisoner's dilemma comes up in all sorts of situations. For instance, instead of prisoners our players could be, say, oil companies. If both set a high price they can sell for a high price, but each one has an incentive to undercut, in which case he will capture the entire market. The equilibrium outcome is for each company to charge a low price.
The prisoner's dilemma isn't the only game with a dominant strategy equilibrium. Here's a more complicated one. Can you tell which strategy is dominant? It's A for player 1, and E for player 2. So the dominant strategy equilibrium is A, E.

http://economicsdetective.com/
Game theory is the study of human behaviour in strategic settings. It is used to solve some of the harder problems in economics.
So what is a game? To have a game, you need at least two players, sometimes called agents, or, if you want to be really crazy, people. And you need payoffs for the players, you need to define the outcomes they can potentially get depending on how the game unfolds. And finally, you need rules for the game.
Now, it's not always obvious how people will behave, even with players, payoffs, and rules clearly defined. That's why game theorists have a number of solution concepts for games, including the dominant strategy equilibrium, the Nash equilibrium, the subgame perfect Nash equilibrium, the Bayesian equilibrium, and the weak perfect Bayesian equilibrium.
The most basic solution concept is the dominant strategy equilibrium. In a game, each player can have any number of possible strategies. One strategy strictly dominates another strategy if the player is always better off under that strategy no matter what other players do. If one strategy strictly dominates every other possible strategy a player could take, that strategy is a strictly dominant strategy. We have a dominant strategy equilibrium when all players play a strictly dominant strategy.
Now let's look at the most famous game in game theory, the Prisoner's Dilemma. There are two prisoners, prisoner 1 and prisoner 2, and they each have a choice. They can testify against the other, or they can keep quiet.
If they both keep quiet, they both get off with a light sentence, which I'll represent with a payoff of 2. Prisoner 1's payoff is on the left, prisoner 2's is on the right. If they both testify, they both get a moderate sentence. I'll represent the moderate sentence by a payoff of 0. Right about now, keeping quiet is looking like the best option, but there's more to this game. If one testifies and the other keeps quiet, the one who testified will get off scot free, and the one who kept quiet will get an extremely harsh sentence; they'll throw the book at him.
Think about this game for a moment. Keeping quiet looks like a pretty good option if both prisoners could promise not to testify. But these prisoners only care about their own self-interest. So, both prisoners may tell the other they pinky swear not to testify, but they won't keep that promise. If prisoner 2 keeps quiet, prisoner 1 is better off testifying. If prisoner 2 testifies, prisoner 1 is better off testifying. Testifying is a dominant strategy for both players, so both testifying is the dominant strategy equilibrium.
The prisoner's dilemma comes up in all sorts of situations. For instance, instead of prisoners our players could be, say, oil companies. If both set a high price they can sell for a high price, but each one has an incentive to undercut, in which case he will capture the entire market. The equilibrium outcome is for each company to charge a low price.
The prisoner's dilemma isn't the only game with a dominant strategy equilibrium. Here's a more complicated one. Can you tell which strategy is dominant? It's A for player 1, and E for player 2. So the dominant strategy equilibrium is A, E.

How Science is Taking the Luck out of Gambling - with Adam Kucharski

From the statisticians forecasting sports scores to the intelligent bots beating human poker players, Adam Kucharski traces the scientific origins of the world's best gambling strategies.
Watch the Q&A here: https://www.youtube.com/watch?v=o0XxbHnf5ro
Subscribe for regular science videos: http://bit.ly/RiSubscRibe
Spanning mathematics, psychology, economics and physics, Adam Kucharski reveals the long and tangled history between betting and science, and explains how gambling shaped everything from probability to game theory, and chaos theory to artificial intelligence.
Adam Kucharski is a Lecturer at LondonSchool of Hygiene and Tropical Medicine where his research focusses on the dynamics of infectious diseases, particularly emerging infections. Prior to this, he got a degree in mathema...

Mental game lessons, from world champion poker coach—Jared Tendler

EP 086: What traders can learn about mental game, from world champion poker coach—Jared Tendler
Jared Tendler is an internationally recognized mental game coach. His clients include world champion poker players, the #1 ranked pool player in the world, professional golfers, and more recently, traders too.
If Jared was to summarize exactly what he does (and what he specializes in), it would be; removing negative and excessive emotion from decision making.
So naturally, this serves as the underlying theme throughout our conversation, but we also discuss higher-level topics like; variance, the major crossovers between high-stakes poker and trading, how psychology has been oversold and when it really matters, plus how to identify various types of “tilt.”
- - - - - -
LINKS
- - - - - -
· Mor...

EP 090: This quants’ approach to designing algo strategies—Michael Halls-Moore, of QuantStart
For this episode I’m joined by Michael Halls-Moore, who runs QuantStart.com—a site well-known by many algorithmic traders.
Prior to trading, Michael studied computational fluid dynamics and was the co-founder of a tech startup, before getting involved a small equity fund as a quant developer—where his key role was cleansing data.
Now, independently, Michael trades his own short-term algorithmic strategies, consults to hedge funds on machine learning and quant infrastructure, and also has a keen interest in space exploration.
We discussed a whole range of topics, including; the need for quality data, thinking about risk from a portfolio level, trading multiple automated strategies, the role of ...

published: 19 Sep 2016

The State of Techniques for Solving Large Imperfect-Information Games, Including Poker

The ability to computationally solve imperfect-information games has a myriad of future applications ranging from auctions, negotiations, and (cyber)security settings to medical domains. A dramatic scalability leap has occurred in the capability to solve such games over the last nine years, fueled in large part by the AnnualComputerPokerCompetition. I will discuss the key, domain-independent, techniques that enabled this leap, including automated abstraction techniques and approaches for mitigating the issues that they raise, new equilibrium-finding algorithms, safe opponent exploitation methods, techniques that use qualitative knowledge as an extra input, and endgame solving techniques. I will also include new results on 1) developing the world’s best Heads-Up No-Limit Texas Hold'em po...

[Game Algorithms] 06 - Hiding AI Basics

Welcome to a new series covering Game Algorithms. This series has similarities to the Video GameMathematicsSeries, but this covers Game Algorithms.
Welcome to a new tutorial for the Game Algorithms series. In this tutorial, I will go over an algorithm that will be used to determine HidingArtificial Intelligence. This is the introduction to Hiding AI and this is a very basic algorithm for this concept. We will cover a more advanced algorithm in a future video.

published: 16 Oct 2016

POKER-PLAYING BOTS

A new podcast serial Cast IT is launced. MeetAssociate Professor Thore Husfeldt from IT University as host while he talks to other researchers about the fundations of IT. The podcast is available to subscribe on Itunes and ITUChannel.
In early 2017, two independent research teams announced progress in artificial intelligence: Libratus from Carnagie Mellon University and DeepStack from University of Alberta. Computer programs are now able to beat the best human players in the two-player card game Heads-Up No LimitTexas’ Hold-Em Poker.
But what are poker-playing bots? And how do they work work?
In this podcast Thore Husfeldt talks to Associate Professor Troels Bjerre Lund, IT University of Copenhagen, a researcher in algorithmic game theory and a leading expert on artificial intellige...

published: 20 Mar 2017

Quantitative Finance & Python Programming | Yves Hilpisch

EP 084: Quantitative finance and programming trading strategies w/ Yves Hilpisch, The Python Quants
Dr. Yves Hilpisch is the founder of The Python Quants.
TPQ do a lot of good for those involved in quantitative finance, they; frequently host meet-ups and workshops, have developed platforms and analytics libraries, and often contract to exchanges, banks and hedge funds for custom Python development.
Yves is also a three-time published author, with his most notable title probably being “Python for Finance” which was released through O’Reilly. He regularly gives presentations and speaks at events on the subject of quant finance, and lectures at Universities too.
Over the next sixty minutes, you’ll hear us unpack many subjects related to being a quant and why programming in Python can be a...

Money & Speed: Inside the Black Box is a thriller based on actual events that takes you to the heart of our automated world. Based on interviews with those directly involved and data visualizations up to the millisecond, it reconstructs the flash crash of May 6th 2010: the fastest and deepest U.S. stock market plunge ever.
Money & Speed: Inside the Black Box is developed by filmmaker Marije Meerman in close collaboration with design studio Catalogtree. This explorative documentary is a marriage of strong storytelling and meticulous visual analysis. This film is also available in English as an iPad-app in the Apple app store as the world's first Touch Doc.
TO THE APPSTORE:
https://itunes.apple.com/us/app/money-speed-inside-black-box/id424796908?mt=8
Originally broadcasted by VPRO in 2...

SF Bitcoin Devs Seminar: 51% Attacks: Pools and Game Theory

Martin Köppelmann presented on 51% Attacks—Pools and Game Theory.
Martin is the founder of Fairlay, the largest Bitcoin prediction market and betting exchange. He has a developer background and used to be a poker professional and coach for poker and game theory. Martin is also working on a basic income based currency on Ethereum.

Algorithmic Game Theory, Lecture 17 (No-Regret Dynamics)

How Science is Taking the Luck out of Gambling - with Adam Kucharski

From the statisticians forecasting sports scores to the intelligent bots beating human poker players, Adam Kucharski traces the scientific origins of the world'...

From the statisticians forecasting sports scores to the intelligent bots beating human poker players, Adam Kucharski traces the scientific origins of the world's best gambling strategies.
Watch the Q&A here: https://www.youtube.com/watch?v=o0XxbHnf5ro
Subscribe for regular science videos: http://bit.ly/RiSubscRibe
Spanning mathematics, psychology, economics and physics, Adam Kucharski reveals the long and tangled history between betting and science, and explains how gambling shaped everything from probability to game theory, and chaos theory to artificial intelligence.
Adam Kucharski is a Lecturer at LondonSchool of Hygiene and Tropical Medicine where his research focusses on the dynamics of infectious diseases, particularly emerging infections. Prior to this, he got a degree in mathematics from the University of Warwick, received a PhD in applied mathematics from the University of Cambridge and had a post-doc position at Imperial College London.
The Ri is on Twitter: http://twitter.com/ri_science
and Facebook: http://www.facebook.com/royalinstitution
and Tumblr: http://ri-science.tumblr.com/
Our editorial policy: http://www.rigb.org/home/editorial-po...
Subscribe for the latest science videos: http://bit.ly/RiNewsletter

From the statisticians forecasting sports scores to the intelligent bots beating human poker players, Adam Kucharski traces the scientific origins of the world's best gambling strategies.
Watch the Q&A here: https://www.youtube.com/watch?v=o0XxbHnf5ro
Subscribe for regular science videos: http://bit.ly/RiSubscRibe
Spanning mathematics, psychology, economics and physics, Adam Kucharski reveals the long and tangled history between betting and science, and explains how gambling shaped everything from probability to game theory, and chaos theory to artificial intelligence.
Adam Kucharski is a Lecturer at LondonSchool of Hygiene and Tropical Medicine where his research focusses on the dynamics of infectious diseases, particularly emerging infections. Prior to this, he got a degree in mathematics from the University of Warwick, received a PhD in applied mathematics from the University of Cambridge and had a post-doc position at Imperial College London.
The Ri is on Twitter: http://twitter.com/ri_science
and Facebook: http://www.facebook.com/royalinstitution
and Tumblr: http://ri-science.tumblr.com/
Our editorial policy: http://www.rigb.org/home/editorial-po...
Subscribe for the latest science videos: http://bit.ly/RiNewsletter

Mental game lessons, from world champion poker coach—Jared Tendler

EP 086: What traders can learn about mental game, from world champion poker coach—Jared Tendler
Jared Tendler is an internationally recognized mental game coac...

EP 086: What traders can learn about mental game, from world champion poker coach—Jared Tendler
Jared Tendler is an internationally recognized mental game coach. His clients include world champion poker players, the #1 ranked pool player in the world, professional golfers, and more recently, traders too.
If Jared was to summarize exactly what he does (and what he specializes in), it would be; removing negative and excessive emotion from decision making.
So naturally, this serves as the underlying theme throughout our conversation, but we also discuss higher-level topics like; variance, the major crossovers between high-stakes poker and trading, how psychology has been oversold and when it really matters, plus how to identify various types of “tilt.”
- - - - - -
LINKS
- - - - - -
· More interviews: https://chatwithtraders.com
· Free resources: https://chatwithtraders.com/resources
· Sponsored by Technician: http://technicianapp.com/
· Twitter: https://twitter.com/chatwithtraders
· Facebook: http://facebook.com/chatwithtraders
· Instagram: https://instagram.com/chatwithtraders_
· Soundcloud: https://soundcloud.com/chat-with-traders
· Stitcher: http://www.stitcher.com/podcast/chat-with-traders

EP 086: What traders can learn about mental game, from world champion poker coach—Jared Tendler
Jared Tendler is an internationally recognized mental game coach. His clients include world champion poker players, the #1 ranked pool player in the world, professional golfers, and more recently, traders too.
If Jared was to summarize exactly what he does (and what he specializes in), it would be; removing negative and excessive emotion from decision making.
So naturally, this serves as the underlying theme throughout our conversation, but we also discuss higher-level topics like; variance, the major crossovers between high-stakes poker and trading, how psychology has been oversold and when it really matters, plus how to identify various types of “tilt.”
- - - - - -
LINKS
- - - - - -
· More interviews: https://chatwithtraders.com
· Free resources: https://chatwithtraders.com/resources
· Sponsored by Technician: http://technicianapp.com/
· Twitter: https://twitter.com/chatwithtraders
· Facebook: http://facebook.com/chatwithtraders
· Instagram: https://instagram.com/chatwithtraders_
· Soundcloud: https://soundcloud.com/chat-with-traders
· Stitcher: http://www.stitcher.com/podcast/chat-with-traders

The State of Techniques for Solving Large Imperfect-Information Games, Including Poker

The ability to computationally solve imperfect-information games has a myriad of future applications ranging from auctions, negotiations, and (cyber)security se...

The ability to computationally solve imperfect-information games has a myriad of future applications ranging from auctions, negotiations, and (cyber)security settings to medical domains. A dramatic scalability leap has occurred in the capability to solve such games over the last nine years, fueled in large part by the AnnualComputerPokerCompetition. I will discuss the key, domain-independent, techniques that enabled this leap, including automated abstraction techniques and approaches for mitigating the issues that they raise, new equilibrium-finding algorithms, safe opponent exploitation methods, techniques that use qualitative knowledge as an extra input, and endgame solving techniques. I will also include new results on 1) developing the world’s best Heads-Up No-Limit Texas Hold'em poker program, 2) theory that enables abstraction that gives solution quality guarantees, 3) techniques for hot starting equilibrium finding, 4) simultaneous abstraction and equilibrium finding, and 5) theory that improves gradient-based equilibrium finding. I will also cover the Brains vs AI competition that I recently organized where our AI, Claudico, challenged four of the top-10 human pros in Heads-Up No-Limit Texas Hold'em for 80,000 hands. (The talk covers joint work with many co-authors, mostly NoamBrown, Sam Ganzfried, and Christian Kroer.

The ability to computationally solve imperfect-information games has a myriad of future applications ranging from auctions, negotiations, and (cyber)security settings to medical domains. A dramatic scalability leap has occurred in the capability to solve such games over the last nine years, fueled in large part by the AnnualComputerPokerCompetition. I will discuss the key, domain-independent, techniques that enabled this leap, including automated abstraction techniques and approaches for mitigating the issues that they raise, new equilibrium-finding algorithms, safe opponent exploitation methods, techniques that use qualitative knowledge as an extra input, and endgame solving techniques. I will also include new results on 1) developing the world’s best Heads-Up No-Limit Texas Hold'em poker program, 2) theory that enables abstraction that gives solution quality guarantees, 3) techniques for hot starting equilibrium finding, 4) simultaneous abstraction and equilibrium finding, and 5) theory that improves gradient-based equilibrium finding. I will also cover the Brains vs AI competition that I recently organized where our AI, Claudico, challenged four of the top-10 human pros in Heads-Up No-Limit Texas Hold'em for 80,000 hands. (The talk covers joint work with many co-authors, mostly NoamBrown, Sam Ganzfried, and Christian Kroer.

Welcome to a new series covering Game Algorithms. This series has similarities to the Video GameMathematicsSeries, but this covers Game Algorithms.
Welcome to a new tutorial for the Game Algorithms series. In this tutorial, I will go over an algorithm that will be used to determine HidingArtificial Intelligence. This is the introduction to Hiding AI and this is a very basic algorithm for this concept. We will cover a more advanced algorithm in a future video.

Welcome to a new series covering Game Algorithms. This series has similarities to the Video GameMathematicsSeries, but this covers Game Algorithms.
Welcome to a new tutorial for the Game Algorithms series. In this tutorial, I will go over an algorithm that will be used to determine HidingArtificial Intelligence. This is the introduction to Hiding AI and this is a very basic algorithm for this concept. We will cover a more advanced algorithm in a future video.

A new podcast serial Cast IT is launced. MeetAssociate Professor Thore Husfeldt from IT University as host while he talks to other researchers about the fundations of IT. The podcast is available to subscribe on Itunes and ITUChannel.
In early 2017, two independent research teams announced progress in artificial intelligence: Libratus from Carnagie Mellon University and DeepStack from University of Alberta. Computer programs are now able to beat the best human players in the two-player card game Heads-Up No LimitTexas’ Hold-Em Poker.
But what are poker-playing bots? And how do they work work?
In this podcast Thore Husfeldt talks to Associate Professor Troels Bjerre Lund, IT University of Copenhagen, a researcher in algorithmic game theory and a leading expert on artificial intelligence for poker.

A new podcast serial Cast IT is launced. MeetAssociate Professor Thore Husfeldt from IT University as host while he talks to other researchers about the fundations of IT. The podcast is available to subscribe on Itunes and ITUChannel.
In early 2017, two independent research teams announced progress in artificial intelligence: Libratus from Carnagie Mellon University and DeepStack from University of Alberta. Computer programs are now able to beat the best human players in the two-player card game Heads-Up No LimitTexas’ Hold-Em Poker.
But what are poker-playing bots? And how do they work work?
In this podcast Thore Husfeldt talks to Associate Professor Troels Bjerre Lund, IT University of Copenhagen, a researcher in algorithmic game theory and a leading expert on artificial intelligence for poker.

EP 084: Quantitative finance and programming trading strategies w/ Yves Hilpisch, The Python Quants
Dr. Yves Hilpisch is the founder of The Python Quants.
TPQ do a lot of good for those involved in quantitative finance, they; frequently host meet-ups and workshops, have developed platforms and analytics libraries, and often contract to exchanges, banks and hedge funds for custom Python development.
Yves is also a three-time published author, with his most notable title probably being “Python for Finance” which was released through O’Reilly. He regularly gives presentations and speaks at events on the subject of quant finance, and lectures at Universities too.
Over the next sixty minutes, you’ll hear us unpack many subjects related to being a quant and why programming in Python can be a useful skill to have in your toolbox.
- - - - - -
LINKS
- - - - - -
· More interviews: https://chatwithtraders.com
· Free resources: https://chatwithtraders.com/resources
· Twitter: https://twitter.com/chatwithtraders
· Facebook: http://facebook.com/chatwithtraders
· Instagram: https://instagram.com/chatwithtraders_
· Soundcloud: https://soundcloud.com/chat-with-traders
· Stitcher: http://www.stitcher.com/podcast/chat-with-traders

EP 084: Quantitative finance and programming trading strategies w/ Yves Hilpisch, The Python Quants
Dr. Yves Hilpisch is the founder of The Python Quants.
TPQ do a lot of good for those involved in quantitative finance, they; frequently host meet-ups and workshops, have developed platforms and analytics libraries, and often contract to exchanges, banks and hedge funds for custom Python development.
Yves is also a three-time published author, with his most notable title probably being “Python for Finance” which was released through O’Reilly. He regularly gives presentations and speaks at events on the subject of quant finance, and lectures at Universities too.
Over the next sixty minutes, you’ll hear us unpack many subjects related to being a quant and why programming in Python can be a useful skill to have in your toolbox.
- - - - - -
LINKS
- - - - - -
· More interviews: https://chatwithtraders.com
· Free resources: https://chatwithtraders.com/resources
· Twitter: https://twitter.com/chatwithtraders
· Facebook: http://facebook.com/chatwithtraders
· Instagram: https://instagram.com/chatwithtraders_
· Soundcloud: https://soundcloud.com/chat-with-traders
· Stitcher: http://www.stitcher.com/podcast/chat-with-traders

SF Bitcoin Devs Seminar: 51% Attacks: Pools and Game Theory

Martin Köppelmann presented on 51% Attacks—Pools and Game Theory.
Martin is the founder of Fairlay, the largest Bitcoin prediction market and betting exchang...

Martin Köppelmann presented on 51% Attacks—Pools and Game Theory.
Martin is the founder of Fairlay, the largest Bitcoin prediction market and betting exchange. He has a developer background and used to be a poker professional and coach for poker and game theory. Martin is also working on a basic income based currency on Ethereum.

Martin Köppelmann presented on 51% Attacks—Pools and Game Theory.
Martin is the founder of Fairlay, the largest Bitcoin prediction market and betting exchange. He has a developer background and used to be a poker professional and coach for poker and game theory. Martin is also working on a basic income based currency on Ethereum.

Deep Algo Tutorial #1 - Poker Game Algorithms

WARNING : This video is not up to date!
If you want to see the last version of DeepAlgo live : ask a free demo on DeepAlgo.com
Deep Algo is a SaaS platform based on 100% automatic algorithm extraction technology.
This Tutorial explains how you can Understand the source code of a poker game.

57:33

How Science is Taking the Luck out of Gambling - with Adam Kucharski

From the statisticians forecasting sports scores to the intelligent bots beating human pok...

How Science is Taking the Luck out of Gambling - with Adam Kucharski

From the statisticians forecasting sports scores to the intelligent bots beating human poker players, Adam Kucharski traces the scientific origins of the world's best gambling strategies.
Watch the Q&A here: https://www.youtube.com/watch?v=o0XxbHnf5ro
Subscribe for regular science videos: http://bit.ly/RiSubscRibe
Spanning mathematics, psychology, economics and physics, Adam Kucharski reveals the long and tangled history between betting and science, and explains how gambling shaped everything from probability to game theory, and chaos theory to artificial intelligence.
Adam Kucharski is a Lecturer at LondonSchool of Hygiene and Tropical Medicine where his research focusses on the dynamics of infectious diseases, particularly emerging infections. Prior to this, he got a degree in mathematics from the University of Warwick, received a PhD in applied mathematics from the University of Cambridge and had a post-doc position at Imperial College London.
The Ri is on Twitter: http://twitter.com/ri_science
and Facebook: http://www.facebook.com/royalinstitution
and Tumblr: http://ri-science.tumblr.com/
Our editorial policy: http://www.rigb.org/home/editorial-po...
Subscribe for the latest science videos: http://bit.ly/RiNewsletter

Mental game lessons, from world champion poker coach—Jared Tendler

EP 086: What traders can learn about mental game, from world champion poker coach—Jared Tendler
Jared Tendler is an internationally recognized mental game coach. His clients include world champion poker players, the #1 ranked pool player in the world, professional golfers, and more recently, traders too.
If Jared was to summarize exactly what he does (and what he specializes in), it would be; removing negative and excessive emotion from decision making.
So naturally, this serves as the underlying theme throughout our conversation, but we also discuss higher-level topics like; variance, the major crossovers between high-stakes poker and trading, how psychology has been oversold and when it really matters, plus how to identify various types of “tilt.”
- - - - - -
LINKS
- - - - - -
· More interviews: https://chatwithtraders.com
· Free resources: https://chatwithtraders.com/resources
· Sponsored by Technician: http://technicianapp.com/
· Twitter: https://twitter.com/chatwithtraders
· Facebook: http://facebook.com/chatwithtraders
· Instagram: https://instagram.com/chatwithtraders_
· Soundcloud: https://soundcloud.com/chat-with-traders
· Stitcher: http://www.stitcher.com/podcast/chat-with-traders

1:30:10

The State of Techniques for Solving Large Imperfect-Information Games, Including Poker

The ability to computationally solve imperfect-information games has a myriad of future ap...

The State of Techniques for Solving Large Imperfect-Information Games, Including Poker

The ability to computationally solve imperfect-information games has a myriad of future applications ranging from auctions, negotiations, and (cyber)security settings to medical domains. A dramatic scalability leap has occurred in the capability to solve such games over the last nine years, fueled in large part by the AnnualComputerPokerCompetition. I will discuss the key, domain-independent, techniques that enabled this leap, including automated abstraction techniques and approaches for mitigating the issues that they raise, new equilibrium-finding algorithms, safe opponent exploitation methods, techniques that use qualitative knowledge as an extra input, and endgame solving techniques. I will also include new results on 1) developing the world’s best Heads-Up No-Limit Texas Hold'em poker program, 2) theory that enables abstraction that gives solution quality guarantees, 3) techniques for hot starting equilibrium finding, 4) simultaneous abstraction and equilibrium finding, and 5) theory that improves gradient-based equilibrium finding. I will also cover the Brains vs AI competition that I recently organized where our AI, Claudico, challenged four of the top-10 human pros in Heads-Up No-Limit Texas Hold'em for 80,000 hands. (The talk covers joint work with many co-authors, mostly NoamBrown, Sam Ganzfried, and Christian Kroer.

How ‘Black Box’ Algorithms are Assisting a New Generation of Criminals

When Siri helped a young criminal nearly get away with murder, future crimes expert Marc Goodman realized how algorithms had become co-conspirators in a new age of digital crime. Goodman's latest book is "FutureCrimes: Inside the Digital Underground and the Battle for Our ConnectedWorld" (http://goo.gl/tw9EIi).
Read more at BigThink.com: http://bigthink.com/videos/marc-goodman-on-artificial-intelligence-and-the-future-of-crime
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Transcript - There was a case recently in Florida where a teenager was arrested for murder. He was a student at the University of Florida and he was accused of murdering his roommate and best friend. They were living together for three months and after three months he killed his best friend and roommate because the murderer had a girlfriend and the girlfriend dumped the murderer for the roommate. As it turns out when the kid murdered his roommate he had a problem. He didn’t know where to bury the dead body. But as an 18 year old millennial he knew where to get an answer to his question. He asked Siri where can I bury a dead body. And it turns out Siri answered his question and proposed minds, dumps, reservoirs, swamps and rivers. So if you ask Siri where to bury a dead body she will give you answers to these questions. So in the old days, you know, we used to talk about Bonnie and Clyde. But we’ve entered the age of Siri and Clyde where clearly algorithms will be unnamed co-conspirators in younger criminals carrying out attacks.
AI is being used across the board in our society via algorithms, right. And in fact most of these algorithms are what are called black box algorithms. There’s a HarvardProfessor called FrankPasquale who wrote a whole book on black box algorithms that talks about how little we know about these algorithms and what some of the dangers might be. For example there’s an algorithm that determines your credit score, FICO. What goes into it exactly and precisely nobody knows. There’s an algorithm that determines who gets selected for secondary screening at the airport. Maybe it’s because you bought a one way ticket? Maybe it’s because you paid in cash? Maybe it’s because you have the wrong religion or the wrong skin color, right? We don’t know clearly at all what is being encoded into these algorithms. And so that opens up the door for them to be abused. We saw examples of this on Wall Street with Flash Boys, right, on rapid trading on Wall Street. The fact of the matter is only a miniscule amount of trading on Wall Street is carried out by human beings. The overwhelming majority of trading is algorithmic in nature. It’s preprogrammed so that if the price of soybeans goes down all these additional steps will take place. If some event happens in the world traders will buy or sell based upon that information. And it all goes so fast there’s no time for human intervention. And we saw an implication of this recently when the Associated Press Twitter feed was hacked a few years ago. Somebody took over the official AP Twitter feed and they put out a tweet from the official site that said breaking news, explosions at the White House, President Obama injured. Now it turns out that never happened but all the algorithms that are monitoring the Net looking for the latest news that they can trade on picked up on it immediately from a trusted source. And because they perceived a terrorist attack that caused the market a massive, massive sell off. In just three minutes because of this one tweet the market fell 136 billion dollars, 136 billion dollars of valuation was evaporated in 180 seconds just because of one wayward tweet. And that was carried out by an organization known as the Syrian Electronic Army. They’re backed, trained and funded by the Iranian government. Now in this particular case they did that for the purposes of mischief. But they also could have shorted the market at the same time and made a lot of money on this. So our algorithms are going way faster than we realize and they’re running things that we don’t even understand. For example when you go for an MRI exam in the hospital that MRI via its algorithms are actually interpreting the data for your radiologist in many ways before they even read that. When you fly on autopilot on an airplane which is probably more than 90 percent of your flight it’s an algorithm that’s running the plane. And all of those can be hacked. Much of the attacks that occur in cyberspace whether they be virus attacks, ransomware, denial of services attacks are all scripted to run which means a computer hacker or criminal writes some code and the code goes off and carries out the crime. Which means crime can scale and it can scale exponentially. And that’s why we’ve seen some massive uptick.

Intro to Game Theory and the Dominant Strategy Equilibrium

http://economicsdetective.com/
Game theory is the study of human behaviour in strategic settings. It is used to solve some of the harder problems in economics.
So what is a game? To have a game, you need at least two players, sometimes called agents, or, if you want to be really crazy, people. And you need payoffs for the players, you need to define the outcomes they can potentially get depending on how the game unfolds. And finally, you need rules for the game.
Now, it's not always obvious how people will behave, even with players, payoffs, and rules clearly defined. That's why game theorists have a number of solution concepts for games, including the dominant strategy equilibrium, the Nash equilibrium, the subgame perfect Nash equilibrium, the Bayesian equilibrium, and the weak perfect Bayesian equilibrium.
The most basic solution concept is the dominant strategy equilibrium. In a game, each player can have any number of possible strategies. One strategy strictly dominates another strategy if the player is always better off under that strategy no matter what other players do. If one strategy strictly dominates every other possible strategy a player could take, that strategy is a strictly dominant strategy. We have a dominant strategy equilibrium when all players play a strictly dominant strategy.
Now let's look at the most famous game in game theory, the Prisoner's Dilemma. There are two prisoners, prisoner 1 and prisoner 2, and they each have a choice. They can testify against the other, or they can keep quiet.
If they both keep quiet, they both get off with a light sentence, which I'll represent with a payoff of 2. Prisoner 1's payoff is on the left, prisoner 2's is on the right. If they both testify, they both get a moderate sentence. I'll represent the moderate sentence by a payoff of 0. Right about now, keeping quiet is looking like the best option, but there's more to this game. If one testifies and the other keeps quiet, the one who testified will get off scot free, and the one who kept quiet will get an extremely harsh sentence; they'll throw the book at him.
Think about this game for a moment. Keeping quiet looks like a pretty good option if both prisoners could promise not to testify. But these prisoners only care about their own self-interest. So, both prisoners may tell the other they pinky swear not to testify, but they won't keep that promise. If prisoner 2 keeps quiet, prisoner 1 is better off testifying. If prisoner 2 testifies, prisoner 1 is better off testifying. Testifying is a dominant strategy for both players, so both testifying is the dominant strategy equilibrium.
The prisoner's dilemma comes up in all sorts of situations. For instance, instead of prisoners our players could be, say, oil companies. If both set a high price they can sell for a high price, but each one has an incentive to undercut, in which case he will capture the entire market. The equilibrium outcome is for each company to charge a low price.
The prisoner's dilemma isn't the only game with a dominant strategy equilibrium. Here's a more complicated one. Can you tell which strategy is dominant? It's A for player 1, and E for player 2. So the dominant strategy equilibrium is A, E.

How Science is Taking the Luck out of Gambling - with Adam Kucharski

From the statisticians forecasting sports scores to the intelligent bots beating human poker players, Adam Kucharski traces the scientific origins of the world's best gambling strategies.
Watch the Q&A here: https://www.youtube.com/watch?v=o0XxbHnf5ro
Subscribe for regular science videos: http://bit.ly/RiSubscRibe
Spanning mathematics, psychology, economics and physics, Adam Kucharski reveals the long and tangled history between betting and science, and explains how gambling shaped everything from probability to game theory, and chaos theory to artificial intelligence.
Adam Kucharski is a Lecturer at LondonSchool of Hygiene and Tropical Medicine where his research focusses on the dynamics of infectious diseases, particularly emerging infections. Prior to this, he got a degree in mathematics from the University of Warwick, received a PhD in applied mathematics from the University of Cambridge and had a post-doc position at Imperial College London.
The Ri is on Twitter: http://twitter.com/ri_science
and Facebook: http://www.facebook.com/royalinstitution
and Tumblr: http://ri-science.tumblr.com/
Our editorial policy: http://www.rigb.org/home/editorial-po...
Subscribe for the latest science videos: http://bit.ly/RiNewsletter

Mental game lessons, from world champion poker coach—Jared Tendler

EP 086: What traders can learn about mental game, from world champion poker coach—Jared Tendler
Jared Tendler is an internationally recognized mental game coach. His clients include world champion poker players, the #1 ranked pool player in the world, professional golfers, and more recently, traders too.
If Jared was to summarize exactly what he does (and what he specializes in), it would be; removing negative and excessive emotion from decision making.
So naturally, this serves as the underlying theme throughout our conversation, but we also discuss higher-level topics like; variance, the major crossovers between high-stakes poker and trading, how psychology has been oversold and when it really matters, plus how to identify various types of “tilt.”
- - - - - -
LINKS
- - - - - -
· More interviews: https://chatwithtraders.com
· Free resources: https://chatwithtraders.com/resources
· Sponsored by Technician: http://technicianapp.com/
· Twitter: https://twitter.com/chatwithtraders
· Facebook: http://facebook.com/chatwithtraders
· Instagram: https://instagram.com/chatwithtraders_
· Soundcloud: https://soundcloud.com/chat-with-traders
· Stitcher: http://www.stitcher.com/podcast/chat-with-traders

The State of Techniques for Solving Large Imperfect-Information Games, Including Poker

The ability to computationally solve imperfect-information games has a myriad of future applications ranging from auctions, negotiations, and (cyber)security settings to medical domains. A dramatic scalability leap has occurred in the capability to solve such games over the last nine years, fueled in large part by the AnnualComputerPokerCompetition. I will discuss the key, domain-independent, techniques that enabled this leap, including automated abstraction techniques and approaches for mitigating the issues that they raise, new equilibrium-finding algorithms, safe opponent exploitation methods, techniques that use qualitative knowledge as an extra input, and endgame solving techniques. I will also include new results on 1) developing the world’s best Heads-Up No-Limit Texas Hold'em poker program, 2) theory that enables abstraction that gives solution quality guarantees, 3) techniques for hot starting equilibrium finding, 4) simultaneous abstraction and equilibrium finding, and 5) theory that improves gradient-based equilibrium finding. I will also cover the Brains vs AI competition that I recently organized where our AI, Claudico, challenged four of the top-10 human pros in Heads-Up No-Limit Texas Hold'em for 80,000 hands. (The talk covers joint work with many co-authors, mostly NoamBrown, Sam Ganzfried, and Christian Kroer.

[Game Algorithms] 06 - Hiding AI Basics

Welcome to a new series covering Game Algorithms. This series has similarities to the Video GameMathematicsSeries, but this covers Game Algorithms.
Welcome to a new tutorial for the Game Algorithms series. In this tutorial, I will go over an algorithm that will be used to determine HidingArtificial Intelligence. This is the introduction to Hiding AI and this is a very basic algorithm for this concept. We will cover a more advanced algorithm in a future video.

58:28

POKER-PLAYING BOTS

A new podcast serial Cast IT is launced. Meet Associate Professor Thore Husfeldt from IT U...

POKER-PLAYING BOTS

A new podcast serial Cast IT is launced. MeetAssociate Professor Thore Husfeldt from IT University as host while he talks to other researchers about the fundations of IT. The podcast is available to subscribe on Itunes and ITUChannel.
In early 2017, two independent research teams announced progress in artificial intelligence: Libratus from Carnagie Mellon University and DeepStack from University of Alberta. Computer programs are now able to beat the best human players in the two-player card game Heads-Up No LimitTexas’ Hold-Em Poker.
But what are poker-playing bots? And how do they work work?
In this podcast Thore Husfeldt talks to Associate Professor Troels Bjerre Lund, IT University of Copenhagen, a researcher in algorithmic game theory and a leading expert on artificial intelligence for poker.

Quantitative Finance & Python Programming | Yves Hilpisch

EP 084: Quantitative finance and programming trading strategies w/ Yves Hilpisch, The Python Quants
Dr. Yves Hilpisch is the founder of The Python Quants.
TPQ do a lot of good for those involved in quantitative finance, they; frequently host meet-ups and workshops, have developed platforms and analytics libraries, and often contract to exchanges, banks and hedge funds for custom Python development.
Yves is also a three-time published author, with his most notable title probably being “Python for Finance” which was released through O’Reilly. He regularly gives presentations and speaks at events on the subject of quant finance, and lectures at Universities too.
Over the next sixty minutes, you’ll hear us unpack many subjects related to being a quant and why programming in Python can be a useful skill to have in your toolbox.
- - - - - -
LINKS
- - - - - -
· More interviews: https://chatwithtraders.com
· Free resources: https://chatwithtraders.com/resources
· Twitter: https://twitter.com/chatwithtraders
· Facebook: http://facebook.com/chatwithtraders
· Instagram: https://instagram.com/chatwithtraders_
· Soundcloud: https://soundcloud.com/chat-with-traders
· Stitcher: http://www.stitcher.com/podcast/chat-with-traders

SF Bitcoin Devs Seminar: 51% Attacks: Pools and Game Theory

Martin Köppelmann presented on 51% Attacks—Pools and Game Theory.
Martin is the founder of Fairlay, the largest Bitcoin prediction market and betting exchange. He has a developer background and used to be a poker professional and coach for poker and game theory. Martin is also working on a basic income based currency on Ethereum.

SF Bitcoin Devs Seminar: 51% Attacks: Pools and Ga...

Algorithmic Game Theory, Lecture 17 (No-Regret Dyn...

It turns out that a theory explaining how we might detect parallel universes and prediction for the end of the world was proposed and completed by physicist Stephen Hawking shortly before he died ... &nbsp;. According to reports, the work predicts that the universe would eventually end when stars run out of energy ... ....

In another blow to the Trump administration Monday, the US Supreme Court decided Arizona must continue to issue state driver’s licenses to so-called Dreamer immigrants and refused to hear an effort by the state to challenge the Obama-era program that protects hundreds of thousands of young adults brought into the country illegally as children, Reuters reported ... – WN.com. Jack Durschlag....

Britain’s Royal Astronomical Society announced Monday that an object called 1I/2017 (‘Oumuamua) – the first confirmed asteroid known to have journeyed here from outside our solar system – most likely came from from a binary star system, or two stars orbiting a common center of gravity, EarthSky reported ... They looked at how common these star systems are in the galaxy ... ....

Uber announced on Monday that it was pulling all of its self-driving cars from public roads in Arizona and San Francisco, Toronto, and Pittsburgh after a female pedestrian was reportedly killed after being struck by an autonomous Uber vehicle in Tempe, according to The Verge.&nbsp; ... “We are fully cooperating with local authorities in their investigation of this incident.” ... "Some incredibly sad news out of Arizona....

We reduced our debt, experienced growth at our new poker facility, reduced costs, and continued making progress, mitigating rising hourly wages in Washington. Last quarter, we spoke about the conversion of our Red Dragon facility to a poker-only operation which occurred on October 19th... In addition, two weeks ago, a seven-table poker location about 4 miles south of us, closed down....

Bookmakers' shares were buoyant yesterday after the UK industry regulator provided hope that a crackdown on roulette and poker machines may be more lenient than expected. Ladbrokes Coral and William Hill jumped, fuelled by a Gambling Commission suggestion that the maximum stake on the machines be cut to £30 (€34) or less ... (Bloomberg). ....

The action by the senator and the House members follows the decision by the Justice Department to force RT America to register as a foreign agent and the imposition of algorithms by Facebook, Google and Twitter that steer traffic away from left-wing, anti-war and progressive websites, including Truthdig ... And the situation appears to be growing worse as the algorithms are refined....

File - This Jan. 17, 2017, file photo shows a Facebook logo being displayed in a start-up companies gathering at Paris' Station F, in Paris. A former employee of a Trump-affiliated data-mining firm says it used algorithms that "took fake ... ....

JesseBenton was a rising star among Republican operatives until he was convicted of criminal charges. Now he’s making a comeback. David Pitt/AP ...Martha McSally in the state’s Republican primary. “KelliPac is locking down airtime to before the August primary to make sure that Dr ... That billionaire, banker and professional poker player Andy Beal, wrote KelliPAC a $500,000 check three weeks before the group paid Benton’s firm.Thank You! ... ....

They will support infrastructure managers in addressing performance and meeting consistent workload demands in data. This press release features multimedia ... Notes ... [2] IOPS ... Self Encrypting Drive (SED) supports the AES 256 bit cryptographic algorithm as one of the measurers to protect data confidentiality and safety in case of system theft or system asset disposal. FIPS-validated models support the AES 256-bit cryptographic algorithm ... Tel....

first published at 1.13pm. The City of Cockburn will consider introducing a social media policy for its elected members after a councillor referred to the local government's ReconciliationActionPlan as a "s--t show" on Facebook ... oh wait. no ... "It would appear that Facebook's algorithms decided that they didn't like the addition of "Not" to the front of my name," Mr Separovich said ... ....

THE GOODNEWS..Overlooked stories of incredible women are being added to Wikipedia by dedicated volunteers. Movements like #MeToo are drawing increased attention to the systemic discrimination facing women in a range of professional fields, from Hollywood and journalism, to banking and government ... Wikipedia is the fifth most popular website worldwide ... Gender bias is also an ongoing issue in content development and search algorithms ... ....

TV news has gotten a bad rep, especially with younger generations. Only 8% of those between the ages of 18 and 29 are getting their news from network TV, vs. 49% of those who are ages 65 and older, according to Pew Research. But it's not just young people who may have lost interest in the 24/7 TV news coverage ... Netflix (NASDAQ ... Netflix ... Recent changes to its NewsFeedalgorithm caused the news content in users' feeds to drop to 4% from 5%....